Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	129,421
2	South Dakota	124,697
3	Rhode Island	114,050
4	Utah	112,694
5	Arizona	109,571
6	Tennessee	108,836
7	Oklahoma	104,509
8	Iowa	104,358
9	Wisconsin	104,288
10	Arkansas	103,727
11	Nebraska	102,180
12	Kansas	99,276
13	Alabama	98,085
14	Indiana	96,856
15	Mississippi	96,579
16	South Carolina	94,643
17	Idaho	93,766
18	Nevada	93,661
19	Illinois	91,915
20	Wyoming	91,810
21	Montana	91,155
22	Louisiana	90,322
23	Texas	88,593
24	Georgia	88,576
25	California	88,200
26	Kentucky	87,963
27	New Mexico	86,116
28	Delaware	85,239
29	Florida	85,081
30	New Jersey	83,986
31	Minnesota	83,983
32	Missouri	82,872
33	Massachusetts	80,925
34	Ohio	80,361
35	New York	79,169
36	North Carolina	78,715
37	Alaska	76,561
38	Connecticut	74,983
39	Colorado	72,213
40	West Virginia	71,192
41	Pennsylvania	70,260
42	Virginia	64,436
43	Michigan	62,979
44	Maryland	61,393
45	District of Columbia	55,144
46	New Hampshire	52,058
47	Washington	43,488
48	Puerto Rico	41,088
49	Oregon	35,630
50	Maine	31,638
51	Vermont	21,918
52	Hawaii	18,908

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	712
2	New York	442
3	Delaware	386
4	New Jersey	362
5	Virginia	350
6	Oklahoma	348
7	North Carolina	330
8	Florida	319
9	Mississippi	310
10	Massachusetts	308
11	Kentucky	307
12	Georgia	295
13	New Hampshire	287
14	Texas	285
15	Utah	282
16	Tennessee	265
17	Nebraska	256
18	Pennsylvania	244
19	Alabama	228
20	Vermont	228
21	Ohio	225
22	Colorado	223
23	Arizona	221
24	Arkansas	219
25	West Virginia	217
26	Nevada	212
27	California	208
28	New Mexico	198
29	Iowa	192
30	Indiana	191
31	District of Columbia	181
32	Louisiana	177
33	Maryland	171
34	South Dakota	171
35	Minnesota	163
36	Illinois	161
37	Montana	151
38	Wisconsin	144
39	Kansas	139
40	Rhode Island	138
41	Maine	116
42	Missouri	107
43	North Dakota	106
44	Puerto Rico	101
45	Idaho	100
46	Oregon	94
47	Michigan	93
48	Wyoming	90
49	Washington	87
50	Connecticut	78
51	Alaska	71
52	Hawaii	42

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,527
2	New York	2,335
3	Massachusetts	2,246
4	Mississippi	2,171
5	Rhode Island	2,161
6	South Dakota	2,084
7	Connecticut	2,070
8	Arizona	2,057
9	Louisiana	1,998
10	North Dakota	1,917
11	Alabama	1,884
12	Indiana	1,808
13	Pennsylvania	1,807
14	Illinois	1,745
15	Arkansas	1,744
16	New Mexico	1,683
17	Iowa	1,659
18	Michigan	1,612
19	Tennessee	1,587
20	South Carolina	1,553
21	Nevada	1,528
22	Kansas	1,497
23	Georgia	1,450
24	Texas	1,429
25	Ohio	1,398
26	District of Columbia	1,387
27	Florida	1,339
28	Delaware	1,317
29	Missouri	1,283
30	Maryland	1,249
31	Montana	1,242
32	West Virginia	1,233
33	California	1,190
34	Wisconsin	1,158
35	Minnesota	1,142
36	Wyoming	1,117
37	Nebraska	1,097
38	Colorado	1,028
39	Oklahoma	1,016
40	Idaho	1,010
41	North Carolina	1,003
42	Kentucky	995
43	New Hampshire	833
44	Virginia	821
45	Washington	622
46	Puerto Rico	600
47	Utah	559
48	Oregon	506
49	Maine	482
50	Alaska	370
51	Vermont	302
52	Hawaii	298

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Ohio	107
2	Alabama	15
3	Arizona	13
4	Georgia	10
5	Massachusetts	10
6	South Carolina	10
7	California	9
8	Mississippi	8
9	Texas	8
10	Arkansas	7
11	Delaware	7
12	Indiana	7
13	Nevada	7
14	New Mexico	7
15	New York	7
16	Florida	6
17	Kansas	6
18	Oklahoma	6
19	Kentucky	5
20	North Carolina	5
21	Oregon	5
22	Pennsylvania	5
23	Rhode Island	5
24	South Dakota	5
25	Tennessee	5
26	Iowa	4
27	Maryland	4
28	Missouri	4
29	New Jersey	4
30	West Virginia	4
31	Illinois	3
32	Louisiana	3
33	Michigan	3
34	New Hampshire	3
35	Colorado	2
36	Connecticut	2
37	District of Columbia	2
38	Idaho	2
39	Virginia	2
40	Maine	1
41	Minnesota	1
42	Montana	1
43	Nebraska	1
44	Puerto Rico	1
45	Utah	1
46	Washington	1
47	Wisconsin	1
48	Alaska	0
49	Hawaii	0
50	North Dakota	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	318,759	1	99
Chattahoochee	Georgia	265,242	2	99
Bent	Colorado	254,796	3	99
Lincoln	Arkansas	239,558	4	99
Lake	Tennessee	237,030	5	99
Davidson	Tennessee	126,014	229	92
Richland	South Carolina	93,328	1232	60
York	South Carolina	87,138	1547	50
Orange	California	80,892	1837	41
Pierce	Washington	40,612	2903	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Iron	Wisconsin	6,682	3	99
Buffalo	South Dakota	6,626	4	99
Galax city	Virginia	6,617	5	99
Davidson	Tennessee	1,165	1931	38
Orange	California	1,126	1974	37
Richland	South Carolina	1,121	1983	36
York	South Carolina	1,043	2095	33
Pierce	Washington	560	2682	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.